An Evaluation of Banana Macropropagation Techniques for Producing Pig Fodder in Northern Thailand
Bibliographic record
Abstract
Smallholder farmers raising pigs in northern Thailand rely heavily on banana stalks as a fermented feed source, but struggle to reproduce banana plants fast enough to keep up with consumption. This study evaluated a variety of techniques for rapidly multiplying banana plants, using techniques appropriate and affordable to smallholder farmers in order to help meet this demand. Propagation techniques of Musa (ABB) cv. ‘Kluai Nam Wa’ were conducted in greenhouse and field experiments in both lowland and upland areas of Chiang Mai Province, Thailand. In greenhouse experiments, six treatments were conducted during the dry and rainy seasons, while five different treatments were compared in the field. Treatments used various methods of mechanical injury or application of benzyl aminopurine (BA) to induce plantlet differentiation. Number of plantlets to emerge, days to emergence, and circumference of plantlets were observed over a 90-day period. Results indicate that time of year plays an important role in the macropropagation of bananas, as significantly higher numbers of plantlets emerged during the rainy season. Plantlets emerged in 65 days, on average, during the dry season, but took only 54 days during the rainy season. During the rainy season, the presence of BA produced more plantlets than the other treatments, but during the dry season, there were no differences among treatments. Overall, the number of plantlets produced in all treatments evaluated was very low; however we believe this research is an important contribution to the literature and acknowledge that there exists significant opportunity to capitalize on the low-cost appropriate technology benefits that macropropagation of bananas can deliver to smallholder farmers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".